https://github.com/bhklab/longarray
longArray Example
https://github.com/bhklab/longarray
Last synced: 11 months ago
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longArray Example
- Host: GitHub
- URL: https://github.com/bhklab/longarray
- Owner: bhklab
- Created: 2018-07-25T18:26:43.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-08-06T14:36:03.000Z (over 6 years ago)
- Last Synced: 2024-12-30T03:23:36.649Z (about 1 year ago)
- Language: R
- Size: 9.37 MB
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
---
title: "Data representation for combinatorial pharmacogenomics"
author: "Vincent J. Carey, stvjc at channing.harvard.edu"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{Data representation for combinatorial pharmacogenomics}
%\VignetteEncoding{UTF-8}
output:
BiocStyle::html_document:
highlight: pygments
number_sections: yes
theme: united
toc: yes
---
```{r setup,echo=FALSE,results="hide"}
suppressWarnings({
suppressPackageStartupMessages({
library(pharmGxComb)
library(BiocStyle)
library(ggplot2)
library(magrittr)
library(dplyr)
})
})
```
# Introduction
Consider the following table representing
combination drug experimental design **for a given cell line**:
```{r lkd}
library(pharmGxComb)
data(dc)
table(dc$drugA_name[1:5000],
dc$drugB_name[1:5000])[1:5,1:5]
```
This is a small slice of data derivable from
`r nrow(dc)` records in `dc`.
We can get an informal look at the effects on viability
using a simple ggplot2 call in `viabDots`.
```{r lkdd}
library(dplyr)
library(magrittr)
viabDots(dc, drugA="5-FU", drugB="ABT-888", incell_line="CAOV3")
```
A very naive test of interaction between 5-FU and ABT-888 concentrations
on viability in CAOV3 is given with
```{r lkreg}
summary(viabReg(dc, drugA="5-FU", drugB="ABT-888", incell_line="CAOV3"))
```
# Thoughts on data structure and use cases
Drugs and cell lines are the main experimental factors. There
is a single assay for viability. We may have genomic data as well,
but the design underlying the genomic assays will determine
how to think about data structure.
At this stage it does not seem clear how to reuse existing Bioc
classes. Here are a few considerations, to be weighed against
highest scientific priorities.
- Ordering cell lines with respect to sensitivity to a given
combination
```{r ordcell,cache=TRUE}
all_lines = unique(dc$cell_line)
all_regs = lapply(all_lines, function(x)
summary(viabReg(dc, drugA="5-FU", drugB="ABT-888", incell_line=x))$coef)
names(all_regs) = all_lines
all_regs_t = sapply(all_regs, function(x)x[4,3])
all_regs_p = sapply(all_regs, function(x)x[4,4])
rep = rbind(all_regs_t, all_regs_p)
rep[,order(rep[2,])[1:5]]
```
- Assessing consistency of combination effects on **related cell lines**.
In this case some aspects of the cell line ontology might
be helpful. How many tokens for cell line in
the test data can be mapped to CLO?
```{r lklklk}
uu = unique(dc$cell_line)
clo = ontoProc::getCellLineOnto()
ii = lapply(uu, function(x) grep(x, clo$name, ignore.case=TRUE, value=TRUE))
jj = ii[sapply(ii,length)>0]
names(jj) = uu[sapply(ii,length)>0]
jj
```
So `r length(jj)`/`r length(uu)` is the fraction of
cell line tokens "directly mappable" to cell line
ontology terms.
- Assessing consistency of effects among related compounds.
Here the ChEBI compound ontology could help define
"distances" between compounds; there are surely
more precise approaches.
```{r zzz}
ch = ontoProc::getChebiOnto()
mm = lapply(alld, function(x) grep(x, ch$name, value=TRUE))
nn = mm[which(sapply(mm, function(x)length(x)>0))]
names(nn) = alld[which(sapply(mm, function(x)length(x)>0))]
nn
```
There are not many direct matches. When we have a tag,
we can find out about the compound class.
```{r lklk}
ch$name[ch$parent[["CHEBI:8988"]]]
```
With standard naming, we can group compounds more effectively.
# Summary
We need to elaborate the key use cases. The long
table representation seems very useful but it does
not immediately fit with the key Bioc classes.
It may be worth pointing out that there is an
approach in restfulSE for lazily converting a long
table layout to a "delayed" SummarizedExperiment.
The back end is BigQuery so you will need to be
able to authenticate to make use of the isb-cgc "project".
`library(restfulSE); example(BQ3_Array)` and
the related source spell this out. It is clumsy.